Restricted accessResearch articleFirst published online 2019-09-09
The generalized dice similarity measures for evaluating the development level of the mass sports culture organization with 2-tuple linguistic information
In this paper, we shall present some novel Dice similarity measures of 2-tuple linguistic variables and the generalized Dice similarity measures of 2-tuple linguistic variables and indicates that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures-based multiple attribute group decision making models with 2-tuple linguistic variables. Then, we apply the generalized Dice similarity measures between 2-tuple linguistic variables for evaluating the development level of the mass sports culture organization. Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for evaluating the development level of the mass sports culture organization.
WangJ.H. and HaoJ.Y., A new version of 2-tuple. Fuzzy linguistic, representation model for computing with words, Ieee Transactions on Fuzzy Systems14 (2006), 435–445.
2.
WangS.Y., Applying 2-tuple multigranularity linguistic variables to determine the supply performance in dynamic environment based on product-oriented strategy, Ieee Transactions on Fuzzy Systems16 (2008), 29–39.
3.
LiX.D., SmarandacheF., DezertJ. and DaiX.Z., Combination of qualitative information with 2-tuple linguistic representation in dsmt, Journal of Computer Science and Technology24 (2009), 786–797.
4.
LiW.H., YuS.H., PeiH.N., ZhaoC. and TianB.Z., A hybrid approach based on fuzzy ahp and 2-tuple fuzzy linguistic method for evaluation in-flight service quality, Journal of Air Transport Management60 (2017), 49–64.
5.
NieR.X., WangJ.Q. and LiL., A shareholder voting method for proxy advisory firm selection based on 2-tuple linguistic picture preference relation, Applied Soft Computing60 (2017), 520–539.
6.
HuangY.H. and WeiG.W., Todim method for pythagorean 2-tuple linguistic multiple attribute decision making, Journal of Intelligent & Fuzzy Systems35 (2018), 901–915.
7.
WeiG.W., AlsaadiF.E., HayatT. and AlsaediA., Picture 2-tuple linguistic aggregation operators in multiple attribute decision making, Soft Computing22 (2018), 989–1002.
8.
WeiG.W., GaoH. and WeiY., Some q-rung orthopair fuzzy heronian mean operators in multiple attribute decision making, International Journal of Intelligent Systems33 (2018), 1426–1458.
9.
WeiG.W. and ZhangZ.P., Some single-valued neutrosophic bonferroni power aggregation operators in multiple attribute decision making, Journal of Ambient Intelligence and Humanized Computing10 (2019), 863–882.
10.
ZhangS.Q., GaoH., WeiG.W., WeiY. and WeiC., Evaluation based on distance from average solution method for multiple criteria group decision making under picture 2-tuple linguistic environment, Mathematics7 (2019), 243.
11.
HerreraF. and MartinezL., A 2-tuple fuzzy linguistic representation model for computing with words, Ieee Transactions on Fuzzy Systems8 (2000), 746–752.
12.
HerreraF. and MartinezL., A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making, Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics31 (2001), 227–234.
13.
MiC.M., LiuS.F. and YuanX.M., Study on 2-tuple linguistic assessment method based on grey clustering, Journal of Grey System19 (2007), 257–268.
14.
MorenoJ.M., del CastilloJ.M.M., PorcelC. and Herrera-ViedmaE., A quality evaluation methodology for health-related websites based on a 2-tuple fuzzy linguistic approach, Soft Computing14 (2010), 887–897.
15.
WangW.P., Toward developing agility evaluation of mass customization systems using 2-tuple linguistic computing, Expert Systems with Applications36 (2009), 3439–3447.
16.
WeiG.W., Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information, Expert Systems with Applications38 (2011), 4824–4828.
17.
MartinezL. and HerreraF., An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, Applications and challenges, Information Sciences207 (2012), 1–18.
18.
LiuH.C., LiuL. and WuJ., Material selection using an interval 2-tuple linguistic vikor method considering subjective and objective weights, Materials & Design52 (2013), 158–167.
19.
DengX.M., WeiG.W., GaoH. and WangJ., Models for safety assessment of construction project with some 2-tuple linguistic pythagorean fuzzy bonferroni mean operators, Ieee Access6 (2018), 52105–52137.
20.
JiangX.P. and WeiG.W., Some bonferroni mean operators with 2-tuple linguistic information and their application to multiple attribute decision making, Journal of Intelligent & Fuzzy Systems27 (2014), 2153–2162.
21.
LiuH.C., LinQ.L. and WuJ., Dependent interval 2-tuple linguistic aggregation operators and their application to multiple attribute group decision making, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems22 (2014), 717–735.
22.
XuY.J., MaF., TaoF.F. and WangH.M., Some methods to deal with unacceptable incomplete 2-tuple fuzzy linguistic preference relations in group decision making, Knowledge-Based Systems56 (2014), 179–190.
23.
EstrellaF.J., EspinillaM., HerreraF. and MartinezL., Flintstones: A fuzzy linguistic decision tools enhancement suite based on the 2-tuple linguistic model and extensions, Information Sciences280 (2014), 152–170.
24.
WangJ.Q., WangD.D., ZhangH.Y. and ChenX.H., Multi-criteria group decision making method based on interval 2-tuple linguistic information and choquet integral aggregation operators, Soft Computing19 (2015), 389–405.
25.
DongY.C. and Herrera-ViedmaE., Consistency-driven automatic methodology to set interval numerical scales of 2-tuple linguistic term sets and its use in the linguistic gdm with preference relation, Ieee Transactions on Cybernetics45 (2015), 780–792.
26.
DuttaB., GuhaD. and MesiarR., A model based on linguistic 2-tuples for dealing with heterogeneous relationship among attributes in multi-expert decision making, Ieee Transactions on Fuzzy Systems23 (2015), 1817–1831.
27.
QinJ.D. and LiuX.W., 2-tuple linguistic muirhead mean operators for multiple attribute group decision making and its application to supplier selection, Kybernetes45 (2016), 2–29.
28.
ZhangW.C., XuY.J. and WangH.M., A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information, International Journal of Systems Science47 (2016), 389–405.
29.
LiZ.X., GaoH. and WeiG.W., Methods for multiple attribute group decision making based on intuitionistic fuzzy dombi hamy mean operators, Symmetry-Basel10 (2018), 574.
30.
LiZ.X., WeiG.W. and GaoH., Methods for multiple attribute decision making with interval-valued pythagorean fuzzy information, Mathematics6 (2018), 228.
31.
WangJ., WeiG.W. and GaoH., Approaches to multiple attribute decision making with interval-valued 2-tuple linguistic pythagorean fuzzy information, Mathematics6 (2018), 201.
32.
WangJ., WeiG.W. and WeiY., Models for green supplier selection with some 2-tuple linguistic neutrosophic number bonferroni mean operators, Symmetry-Basel10 (2018), 131.
33.
WuL.P., WeiG.W., GaoH. and WeiY., Some interval-valued intuitionistic fuzzy dombi hamy mean operators and their application for evaluating the elderly tourism service quality in tourism destination, Mathematics6 (2018), 294.
34.
TangM., WangJ., LuJ.P., WeiG.W., WeiC. and WeiY., Dual hesitant pythagorean fuzzy heronian mean operators in multiple attribute decision making, Mathematics7 (2019), 344.
35.
TangX.Y. and WeiG.W., Multiple attribute decision-making with dual hesitant pythagorean fuzzy information, Cognitive Computation11 (2019), 193–211.
36.
WeiG.W., Picture fuzzy hamacher aggregation operators and their application to multiple attribute decision making, Fundamenta Informaticae157 (2018), 271–320.
37.
WeiG.W., LuM., TangX.Y. and WeiY., Pythagorean hesitant fuzzy hamacher aggregation operators and their application to multiple attribute decision making, International Journal of Intelligent Systems33 (2018), 1197–1233.
38.
WeiG.W., GaoH., WangJ. and HuangY.H., Research on risk evaluation of enterprise human capital investment with interval-valued bipolar 2-tuple linguistic information, Ieee Access6 (2018), 35697–35712.
39.
LiangZ.Z. and ShiP.F., Similarity measures on intuitionistic fuzzy sets, Pattern Recognition Letters24 (2003), 2687–2693.
40.
SzmidtE. and KacprzykJ., A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning, in: RutkowskiL., SiekmannJ., TadeusiewiczR. and ZadehL.A. (Eds.) Artificial intelligence and soft computing - icaisc2004 (2004), 388–393.
41.
ZhenZ. and Qi-zongW., Similarity measures on intuitionistic fuzzy sets, 2007.
42.
ZhangY.J., MaP.J. and SuX.H., Pattern recognition using interval-valued intuitionistic fuzzy set and its similarity degree, 2009.
43.
YeJ., Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making, Journal of Intelligent & Fuzzy Systems26 (2014), 165–172.
44.
WeiG.W. and WeiY., Similarity measures of pythagorean fuzzy sets based on the cosine function and their applications, International Journal of Intelligent Systems33 (2018), 634–652.
45.
WeiG.W., Some similarity measures for picture fuzzy sets and their applications, Iranian Journal of Fuzzy Systems15 (2018), 77–89.
46.
WangP., WangJ., WeiG.W. and WeiC., Similarity measures of q-rung orthopair fuzzy sets based on cosine function and their applications, Mathematics7 (2019), 340.
47.
YeJ., Multicriteria group decision-making method using the distances-based similarity measures between intuitionistic trapezoidal fuzzy numbers, International Journal of General Systems41 (2012), 729–739.
48.
GuanH.J., GuanS. and ZhaoA.W., Forecasting model based on neutrosophic logical relationship and jaccard similarity, Symmetry-Basel9 (2017).
49.
HwangC.M., YangM.S. and HungW.L., New similarity measures of intuitionistic fuzzy sets based on the jaccard index with its application to clustering, International Journal of Intelligent Systems33 (2018), 1672–1688.
50.
YeJ., FangZ.B. and CuiW.H., Vector similarity measures of q-linguistic neutrosophic variable sets and their multi-attribute decision making method, Symmetry-Basel10 (2018).
51.
YeJ., Multicriteria group decision-making method using vector similarity measures for trapezoidal intuitionistic fuzzy numbers, Group Decision and Negotiation21 (2012), 519–530.
52.
YeJ., Expected value method for intuitionistic trapezoidal fuzzy multicriteria decision-making problems, Expert Systems with Applications38 (2011), 11730–11734.
53.
YeJ., Multicriteria decision-making method using the dice similarity measure based on the reduct intuitionistic fuzzy sets of interval-valued intuitionistic fuzzy sets, Applied Mathematical Modelling36 (2012), 4466–4472.
54.
YeJ., Vector similarity measures of simplified neutrosophic sets and their application in multicriteria decision making, International Journal of Fuzzy Systems16 (2014), 204–211.
55.
YeJ., The generalized dice measures for multiple attribute decision making under simplified neutrosophic environments, Journal of Intelligent & Fuzzy Systems31 (2016), 663–671.
56.
HerreraF. and MartinezL., An approach for combining linguistic and numerical information based on the 2-tuple fuzzy linguistic representation model in decision-making, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems8 (2000), 539–562.
57.
HerreraF. and MartinezL., The 2-tuple linguistic computational model. Advantages of its linguistic description, accuracy and consistency, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems9 (2001), 33–48.
58.
DiceL.R., Measures of the amount of ecologic association between species, Ecology26 (1945), 297–302.
59.
WangJ., WeiG.W. and LuM., An extended vikor method for multiple criteria group decision making with triangular fuzzy neutrosophic numbers, Symmetry-Basel10 (2018), 497.
60.
WeiG.W., GargH., GaoH. and WeiC., Interval-valued pythagorean fuzzy maclaurin symmetric mean operators in multiple attribute decision making, Ieee Access6 (2018), 67866–67884.
61.
WeiG.W., WeiC. and GaoH., Multiple attribute decision making with interval-valued bipolar fuzzy information and their application to emerging technology commercialization evaluation, IEEE Access6 (2018), 60930–60955.
62.
WangJ., GaoH., WeiG.W. and WeiY., Methods for multiple-attribute group decision making with q-rung interval-valued orthopair fuzzy information and their applications to the selection of green suppliers, Symmetry-Basel11 (2019), 56.
63.
WeiG.W., Pythagorean fuzzy hamacher power aggregation operators in multiple attribute decision making, Fundamenta Informaticae166 (2019), 57–85.
64.
EcerF., Third-party logistics (3pls) provider selection via fuzzy ahp and edas integrated model, Technological and Economic Development of Economy24 (2018), 615–634.
65.
KarasanA. and KahramanC., A novel interval-valued neutrosophic edas method: Prioritization of the united nations national sustainable development goals, Soft Computing22 (2018), 4891–4906.
66.
TangX.Y., WeiG.W. and GaoH., Models for multiple attribute decision making with interval-valued pythagorean fuzzy muirhead mean operators and their application to green suppliers selection, Informatica30 (2019), 153v186.
67.
WangJ., GaoH. and WeiG.W., The generalized dice similarity measures for pythagorean fuzzy multiple attribute group decision making, International Journal of Intelligent Systems34 (2019), 1158–1183.
68.
WeiG.W., AlsaadiF.E., HayatT. and AlsaediA., Bipolar fuzzy hamacher aggregation operators in multiple attribute decision making, International Journal of Fuzzy Systems20 (2018), 1–12.
69.
WeiG.W. and GaoH., The generalized dice similarity measures for picture fuzzy sets and their applications, Informatica29 (2018), 107–124.
70.
GaoH., Pythagorean fuzzy hamacher prioritized aggregation operators in multiple attribute decision making, Journal of Intelligent & Fuzzy Systems35 (2018), 2229–2245.
71.
WangJ., WeiG.W. and GaoH., Approaches to multiple attribute decision making with interval-valued 2-tuple linguistic pythagorean fuzzy information, Mathematics6 (2018).
